code: def env_boston(request):
      """Environment fixture using the Boston regression dataset. Parametrizes `holdout_dataset`, so
      all tests using this fixture will be run twice: once with no `holdout_dataset`, and once with a
      `holdout_dataset` constructed using SKLearn's `train_test_split`
      """
      return Environment(
          train_dataset=get_boston_data(),
          results_path=assets_dir,
          target_column="DIS",
          metrics=["r2_score"],
          holdout_dataset=request.param,
          cv_type="KFold",
          cv_params=dict(n_splits=3, random_state=1),
      )
  
file_path: hyperparameter_hunter/tests/integration_tests/feature_engineering/test_support.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Categorical, GBRT
  from hyperparameter_hunter.utils.learning_utils import get_boston_data
  import pytest
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import QuantileTransformer, StandardScaler
label_desc: Use image node_id 1 for calling a global function with example usage: env_boston(request) and returns: Environment